Enhanced Student Performance Prediction Using Data Augmentation with Ensemble Generative Adversarial Network

Objective: To solve the issue of inadequate student data and increase the precision of performance prediction. Methods: This paper presents an innovative Ensemble Generative Adversarial Network (EGAN) model to augment student data and improve the accuracy of predicting student performance. The EGAN integrates two different GAN models: (i) Divergence GAN (DivGAN) and Success-aware GAN (SucGAN). The DivGAN reduces the difference between latent variables and observed data. The SucGAN uses Gumbel-Softmax Relaxation (GSR) to approximate the categorical distribution for creating high-quality data, which solves the imbalance ratio between raw and synthetic data in multiple classes (e.g., Pass, Fail, Distinction, and High Distinction). In contrast, only concentrating on data quality leads to mode collapse issue. So, a Tuna Swarm Optimization (TSO) is employed to stabilize the SucGAN training process and ensure a balance between data quality and diversity while training SucGAN. Thus, the synthetic student data is generated by EGAN and combined with the original dataset to form an augmented dataset. Moreover, this dataset is used by the Student Accomplishment prediction model using the Distinctive Deep Learning (SADDL) model to accurately predict student performance. Findings: The experimental findings illustrate that the EGAN-SADDL attains 94.71% accuracy and 0.0529 Root Mean Square Error (RMSE) in predicting student performance compared to the existing DL models. Novelty: This work expands the dataset by creating synthetic student data using EGAN. By increasing the amount of data available for training the SADDL model, this data augmentation strategy also boosts the model's accuracy and generalization abilities. Keywords: Student's performance prediction, SADDL, GAN, Divergence, Tuna swarm optimizer, Gumbel-Softmax relaxation